METADATA
Hypres Database of Hydraulic Properties of European Soils ver 1.0

J.H.M. Wösten, DLO Winand Staring Centre for Integrated Land, Soil and Water Research (SC-DLO)
P.O. Box 125, 6700 AC Wageningen, The Netherlands
Phone: +31 317 474287
Fax: +31 317 424812
Email: j.h.m.wosten@sc.dlo.nl

Summary

A major obstacle to the wider application of water simulation models is the lack of easily accessible and representative soil hydraulic properties. To overcome this apparent lack of data, a project was initiated to bring together the available hydraulic data on soils, residing within different institutions in Europe, into one central database. This information has been used to derive a set of pedotransfer functions that can provide a satisfactory alternative to costly and time-consuming direct measurements.

A total of 20 institutions from 12 European countries collaborated in establishing the database of HYdraulic PRoperties of European Soils (HYPRES). As a consequence, it was necessary to standardise both the particle-size and the hydraulic data. Standardization of hydraulic data was achieved by fitting the Mualem-van Genuchten model parameters to the individual (h) and K(h) hydraulic properties stored in HYPRES.

The HYPRES database contains information on a total of 5521 soil horizons. Each soil horizon was allocated to one of 11 possible soil textural/pedological classes derived from the 6 FAO texture classes (5 mineral and 1 organic) and the two pedological classes (topsoil and subsoil) recognised within the 1:1,000,000 scale Soil Geographical Database of Eurasia. Then, both class and continuous pedotransfer functions were developed. The class pedotransfer functions were used in combination with the 1:1,000,000 scale Soil Map of Europa to determine the spatial distribution of soil water availability.

Background

An attractive alternative to the direct, expensive and often difficult measurement of hydraulic properties of soils is the estimation by pedotransfer functions. Pedotransfer functions relate hydraulic properties to more easily measured soil data such as soil texture (sand, silt, and clay) organic matter content and/or other data routinely measured by Soil Surveys (Bouma and Van Lanen, 1987).

A prerequisite for deriving such pedotransfer functions is the availability of basic soil data and soil hydraulic properties from a wide range of soils across Europa. Until now, these data were fragmented, of varying degrees of detail and reliability and held by different institutions scattered throughout Europe. However, a group of 20 institutions from 12 European countries recently collaborated to bring together the available measured hydraulic properties held by different institutions in Europe into one central database.

In establishing and using this database a number of specific objectives were identified:

• Development of a flexible database structure capable of holding a wide diversity of soil hydraulic and pedological data and which allows easy manipulation of the data
• Populating the database with soil data from institutions across Europe
• Pre-processing the soil data which includes standardisation of particle-size classes and parameterisation of the hydraulic properties with the Mualem-van Genuchten equations (van Genuchten, 1980)
• Development of both class and continuous pedotransfer functions
• Demonstration of the practical use of the database by linkage with the existing 1:1,000,000 scale Soil Geographical Database of Eurasia (Jamagne et al., 1994)

Data analysis

As there was great diversity in the data being collected and manipulated, it was important to have a database with a relational structure which allowed flexibility in data extraction, for example, using a variety of fields or by a combination of fields. Therefore HYPRES was developed within the Oracle Relational Database Management System™ (Wösten et al., 1998; Wösten et al., 1999). HYPRES comprises six separate tables each of which uses a European standard system of geo-referencing as the primary key and, where appropriate, also the horizon notation as the secondary key.

Figure 1- Structure of the HYPRES database showing the six separate tables

Figure 1 gives the structure of the database. The BASICDATA table contains the 'descriptor' data, for example, information on the soil type, where the soil profile was located and a description of the site and other environmental conditions. The table SOIL_PROPS stores most of the data essential to the derivation of pedotransfer functions such as particle-size class, organic matter contents and bulk densities as well as additional pedological information.

The HYDRAULIC_PROPS table holds only derived or standardised data such as the Mualem-van Genuchten parameters and calculated soil moisture retention and hydraulic conductivities at 14 pre-determined pressure heads. The 'RAW' tables, that is RAWRET, RAWK and RAWPSD, store the data on moisture retention, conductivity and particle-size distributions. These were the data contributed by the institutions and are in their 'raw' state, that is, prior to any standardisation.

The 20 institutions contributed their data in various forms for example, as paper copies of internal reports, or in digital form such as ASCII text, spreadsheets and various database systems. Substantial effort was spent transforming the data into a standard format that would allow easy, computerised manipulation of the data. HYPRES Version 1.0 comprises around 25 Megabytes of data held in six separate data tables and represents 95 different soil types according to the modified FAO soil legend (CEC, 1985) used in the 1:1,000,000 Soil Geographical Database of Eurasia. There are 1777 sampled locations with 5521 samples (including replicates) from 4486 soil horizons. The RAWRET and RAWK tables have over 197,000 (h) and about 120,500 K(h) data pairs respectively.

To achieve compatibility within HYPRES and with other European soil databases, it was decided to standardise the particle-size data to three size limits. Clay is defined as the particle-size fraction < 2 µm, silt as the fraction between 2 and 50 µm and sand as the fraction between 50 and 2000 µm (FAO, 1990; USDA, 1951). Once these particle-size data were in a standard form, they were then stratified according to their texture class and pedology giving 11 classes: 5 topsoil, 5 subsoil and 1 organic class (Nemes et al., 1999). For the definition of organic (Histic) layers see FAO (1990).

Like the soil textural data, the soil hydraulic data were derived by various methods. This has resulted in an unbalanced number of data pairs for the soil samples in HYPRES. Therefore, there was also a necessity to standardise these data prior to the development of pedotransfer functions to reduce the possibility of statistical bias. The volumetric soil water content, , and hydraulic conductivity, K, as functions of pressure head, h, were parameterised with the equations derived by van Genuchten (1980). The nonlinear least-squares optimisation program RETC (van Genuchten et al., 1991) was used to predict the unknown Mualem-van Genuchten parameters (_r, _s, K_s, , l and n) simultaneously from measured water retention and hydraulic conductivity data.

Once the parameterisation was completed, the optimised Mualem-van Genuchten model parameters were used to generate water content and hydraulic conductivity values for the following selected pressure head values: 0, -10, -20, -50, -100, -200, -250, -500, -1000, -2000, -5000, -10000, -15000 and -16000 cm. In this way all soil horizons, regardless of the number of original measured data points, could be represented by an equal weight in the process of development of class pedotransfer functions. The derived data are stored in the HYDRAULIC_PROPS table.

Results and discussion

Pedotransfer functions for each of the 11 classes were derived by firstly using the optimised Mualem-van Genuchten parameters to determine the moisture contents and conductivities at 14 pressure heads as described above. As the(h) and K(h) relationships are log-normally distributed, the geometric mean moisture contents and conductivities at the 14 pressure heads were calculated. In addition to the geometric mean values, the and K values within one standard deviation were calculated. Next the geometric mean values are optimised again with the Mualem-van Genuchten model. The optimised parameters are listed in Table 1. Since these parameters represent the average soil hydraulic properties for a soil texture class they are called class pedotransfer functions. Using the optimised parameters listed in Table 1, moisture contents and conductivities at 14 pressure heads are listed in Table 2.

In addition to the development of class pedotransfer functions, linear regression was also used to investigate the dependency of each model parameter on more easily measured, basic soil properties. The following basic soil properties were used as regressed variables: percentage clay, percentage silt, percentage organic matter; bulk density and also the qualitative variable topsoil or subsoil. The resulting regression model or continuous pedotransfer function consists of various basic soil properties and their interactions, all of which contribute significantly to the description of the transformed model parameters.

Since these pedotransfer functions require point specific soil data instead of class average texture data, they are called continuous pedotransfer functions (Tietje and Tapkenhinrichs, 1993). Table 3 shows the continuous pedotranfer functions derived for the HYPRES database. While class pedo-transfer functions predict the hydraulic properties for rather broadly defined soil texture classes, and therefore do not provide site specific information, continuous pedotransfer functions can be applied in case of more site specific applications.

Application

Throughout the study care was taken to ensure that the HYPRES database and the derived products were compatible with existing EU-wide soil databases and with the 1:1,000,000 Soil Geographical Database of Eurasia (Jamagne et al., 1994). For example, the class pedotransfer functions comprise geometric mean water retention and hydraulic conductivity properties for the 11 soil textural/pedological classes which accord with those used in the Soil Geographical Database. These 11 'building blocks' allow a soil physical interpretation of existing soil maps and thus generate information on the soil physical composition of the unsaturated zone for areas of land (Wösten et al., 1985). Using the class pedotransfer functions, available water capacities were calculated for the different topsoil and subsoil horizons of the Soil Geographical Database. Available water was considered to be the water held between field capacity (pressure head = -50 cm) and wilting point (pressure head = -15000 cm). Each Soil Typological Units (STU) of the Soil Geographical Database was characterised by its topsoil and subsoil textures, soil depths and horizon thickness (King et al., 1994). The amount of available water for each horizon was derived from the appropriate class pedotransfer function multiplied by the thickness of each horizon. Next, the total available water in mm for each STU was calculated by summation of the calculated moisture availability of the appropriate topsoil and subsoil horizons.

Using the estimated values for each STU of the Soil Geographical Database of Eurasia, a map is made of total available water on a European scale (Wösten et al., 1999). This map is just one example of the type of new spatial information that can be generated when the derived pedotransfer functions are used in combination with other existing European soil data. Other possible new products could be a travel time map for solutes and an infiltration rate map for erosion studies.

Conclusions and recommendations

A number of conclusions and recommendations can be drawn from this work.

Conclusions:

• The HYPRES database and its derived pedotransfer functions make it possible to assign soil hydraulic properties to soils with a textural composition comparable to the soils for which these pedotransfer functions have been derived.
• Class pedotransfer functions give the mean hydraulic properties for rather broadly defined soil texture classes. As a consequence, these functions are more suitable for general application and only give limited site-specific information. In contrast, continuous pedotransfer functions are more suitable for site-specific situations and have limited general applicability.
• The number of individual, measured properties varies greatly for the different texture classes. These differences in numbers have consequences for how representative these mean properties are for any particular texture class.
• Classification of measurements is based on texture information of the soil horizons on which the measurements are carried out. This implies, for example, that differences in geological formation or soil structure are not taken into account.
• Use of different measurement techniques by the institutions that contributed soil hydraulic properties, will contribute to the within-class variability. This ‘method-effect’ can not be distinguished from the spatial variability.
• By making use of the 11 texture classes of the 1:1,000,000 scale Soil Geographical Database of Eurasia, the derived pedotransfer functions can be applied on a pan-national scale of 1:1,000,000 or more general.

Recommendations:

• The HYPRES database constitutes a unique source of information on soil hydraulic properties of European soils. Continuing creative and innovative use of this information (e.g. neural networks, other types of correlation, linkage with other international databases) is highly recommended.
• It is recommended that periodic updates of the pedotransfer functions be made when more data become available. The ongoing process of adding new data and updating will result in improvement of the end products and will increase the applicability of the end products for Europe as a whole. In collecting new data, emphasis should be on those countries that until now contributed relatively few data.
• It is recommended that along with periodic updates attention is given to the harmonisation of soil physical measurement techniques among the different institutions. This will minimise the "method-effect" on the within-class variability of the soil hydraulic properties.
• It is expected that the HYPRES database, and the other products arising from this in-depth investment, will be used by many researchers working on European agricultural and environmental issues. It represents the first attempt at standardising the disparate soil hydraulic data from around Europe and it is recommended that national institutions develop a similar approach to the organisation of such data within their own countries.
• It is recommended that soil hydraulic data from countries in Central and Eastern Europe be added to the HYPRES database whenever possible. This is of particular importance as these countries already co-operate in the formation of other soils-related European databases.

References

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Tietje, O. and Tapkenhinrichs, M., 1993. Evaluation of pedo-transfer functions. Soil Sci. Soc. Am. J. 57: 1088-1095.

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Wösten, J.H.M., Lilly, A., Nemes, A. and Le Bas, C., 1998. Using existing soil data to derive hydraulic parameters for simulation models in environmental studies and in land use planning. DLO Winand Staring Centre, Report 157, Wageningen, the Netherlands.

Wösten, J.H.M., Lilly, A., Nemes, A. and Le Bas, C., 1999. Development and use of a database of hydraulic properties of European soils. Accepted by GEODERMA.